Machine Learning Beam Failure Prediction for Faster 5G Recovery
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Solution Overview
Problem
Existing 5G network technologies experience unnecessary delays in declaring beam failure events, particularly in areas with deep fade or blockage, leading to latency issues in critical communications like Ultra-Reliable Low-Latency Communication (URLLC), due to the reliance on pre-defined consecutive beam failure instances for initiating recovery procedures.
Innovation Solution
A method and apparatus utilizing machine learning (ML) models to predict beam failure probability based on user equipment (UE) location and beam failure instance counters, enabling proactive initiation of beam recovery procedures by comparing a calculated probability factor against a threshold, thereby minimizing delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the terminal device waits for N consecutive beam failure instances before declaring beam failure, then false beam failure declarations are reduced, but unnecessary delay is introduced in beam failure recovery
Solution Approach 1:
The system performs preliminary actions by predicting beam failure probability using machine learning models before the terminal device waits for N consecutive beam failure instances. The ML model analyzes historical beam failure data, UE location, and environmental factors to predict imminent beam failures, enabling early initiation of beam recovery procedures while maintaining reliable declaration accuracy
Solution Approach 2:
The patent replaces the mechanical counting mechanism (waiting for N consecutive beam failure instances) with an intelligent prediction system using machine learning models. The ML-based prediction system substitutes the rigid threshold-based approach, enabling dynamic assessment of beam failure probability and triggering recovery procedures based on predicted risk rather than fixed consecutive failure counts
2Loss of time
If the terminal device declares beam failure immediately upon detecting beam failure instances, then beam failure recovery delay is reduced, but false beam failure declarations increase in deep fade and blockage prone areas
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from actual beam failure outcomes and UE location data. The ML model receives feedback about predicted beam failures and actual beam failure occurrences, refining its prediction accuracy over time. This feedback loop enables the system to distinguish between transient deep fade conditions and genuine beam failures, reducing false declarations while maintaining rapid recovery response
Solution Approach 2:
The patent changes the parameter for beam failure declaration from a fixed threshold (N consecutive instances) to a dynamic probability assessment based on multiple parameters including UE location, historical beam failure data, environmental conditions, and signal quality metrics. The ML model dynamically adjusts the beam failure declaration decision based on the synthesized probability, enabling context-aware rapid declaration without increasing false positives
3Loss of time
If machine learning models are used to predict beam failure probability, then beam failure recovery delay is reduced, but device complexity increases
Solution Approach 1:
The machine learning model operates autonomously using self-service principles, continuously training and updating itself using historical beam failure data and UE location information stored in the network. The model automatically performs prediction, decision-making, and adaptation without requiring complex external control mechanisms, reducing the burden on network infrastructure while enabling rapid beam failure recovery
Data Source
AI summary
A method and apparatus (100) for beam failure management for a user equipment (UE) (104) are described. A beam failure instance counter (BFI_counter) is determined, that is indicative of a number of consecutive beam failure instances occurred at the UE (104), at a time instance. A location of the UE (104) is determined at the time instance. A beam failure probability factor is determined, based at least on the location of the UE (104) at the time instance and the BFI_counter. The beam failure probability factor is indicative of a probability of occurrence of a beam failure at the location of the UE (104), after a plurality of further time instances. Further, the beam failure probability factor is compared with a beam failure threshold probability (beamFailureThresholdProb). Thereafter, a beam failure is declared if the beam failure probability factor is higher than the beamFailureThresholdProb.